QRMark: Efficient Image Watermark Detection

• QRMark is an efficient watermark detection system that combines tile-based processing with QR Code–inspired Reed–Solomon error correction to handle large-scale images.
• It leverages a neural network extractor and an RS-aware loss function to maintain high detection accuracy despite tile-level corruption and transformations.
• The system achieves a 2.43× throughput speedup over traditional methods while optimizing GPU and CPU resource utilization for robust, real-time performance.

Searching arXiv for QRMark and closely related image watermark detection work to ground the article in current papers. QRMark is an efficient and adaptive watermark detection system for images, designed to process large-scale uploads from generative models with high throughput and low latency. Its core design combines QR Code–inspired error correction, specifically Reed–Solomon coding, with tile-based detection so that watermark extraction can be parallelized while remaining robust to partial corruption and common image transformations. In end-to-end evaluation, the system is presented as an efficient and adaptive end-to-end method for detecting embedded image watermarks, achieving an average $2.43\times$ inference speedup over a sequential baseline while preserving accuracy and robustness (Zhong et al., 2 Sep 2025).

1. Definition and problem setting

QRMark addresses a systems-level gap in image watermark detection. Existing approaches are described as primarily focusing on improving detection accuracy and robustness under various image transformations and adversarial manipulations, while largely overlooking the efficiency challenges of watermark detection across large-scale image collections (Zhong et al., 2 Sep 2025). QRMark is introduced specifically for this setting: efficient and reliable detection of generated images at scale.

The method is organized around two coupled ideas. At the algorithmic level, it uses Reed–Solomon error correction to mitigate the accuracy degradation introduced by tiling. At the system level, it uses a resource-aware stream allocation policy, tile-based workload interleaving, kernel scheduling across stages, asynchronous Reed–Solomon correction, and preprocessing kernel fusion to improve end-to-end efficiency (Zhong et al., 2 Sep 2025).

A plausible implication is that QRMark is not merely a watermark decoder, but a co-designed algorithm-and-systems framework in which decoding accuracy, GPU utilization, CPU offloading, and pipeline overlap are treated as a single optimization problem.

2. Tile-based watermark detection and embedding model

The watermark detection design is tile-based. Instead of processing the entire image, QRMark partitions each image into tiles, with the example of $64 \times 64$ patches from a $256 \times 256$ image given in the description (Zhong et al., 2 Sep 2025). Detection then proceeds from tile inputs rather than from the full image.

A neural network extractor, denoted $H_D$, is used as a tile-level extractor and is specifically trained or fine-tuned for tile inputs. The extractor is trained via binary cross-entropy and an RS-aware loss so that it is robust to localized corruptions that may occur with tiling (Zhong et al., 2 Sep 2025). The watermark message $m$ is encoded into an error-correcting codeword $m_s$ using Reed–Solomon codes, then embedded tile-wise into the image via fine-tuned LDM decoders $\mathcal{D}_m$ (Zhong et al., 2 Sep 2025).

The detection workflow follows the same decomposition. A tile is fed to the extractor $H_D$, producing a potentially errorful bitstream $m'$. Reed–Solomon decoding is then performed using the Berlekamp–Welch algorithm, after which the corrected message $c_s$ is checked against the expected key (Zhong et al., 2 Sep 2025). The summary given for the method states that one tile suffices for extraction, after which the extractor output is passed through Reed–Solomon correction (Zhong et al., 2 Sep 2025).

This design is explicitly motivated by QR Codes: QR Codes remain readable even if partially damaged due to their error correction, and QRMark is described as mimicking this robustness (Zhong et al., 2 Sep 2025).

3. Reed–Solomon error correction and RS-aware training

QRMark’s error-correction layer is based on Reed–Solomon codes. Before embedding, the message $64 \times 64$0 is padded with $64 \times 64$1 correction symbols, and the decoder is designed to correct up to

$64 \times 64$2

symbol errors during Reed–Solomon decoding (Zhong et al., 2 Sep 2025).

The training objective includes an RS-aware loss that penalizes bit errors above the correctable threshold. The error count is defined as

$64 \times 64$3

and the RS-aware term is

$64 \times 64$4

The final loss is

$64 \times 64$5

where $64 \times 64$6 is a balancing parameter (Zhong et al., 2 Sep 2025).

The appendix-level mathematical description specifies the Reed–Solomon formulation more explicitly. For encoding, given $64 \times 64$7 symbols with $64 \times 64$8 bits per symbol, one interpolates a polynomial $64 \times 64$9 of degree $256 \times 256$0 using Lagrange interpolation so that $256 \times 256$1 for the $256 \times 256$2 message positions, and the codeword is defined by $256 \times 256$3 for all $256 \times 256$4 positions, including message and redundancy. For decoding, one solves for polynomials $256 \times 256$5 and $256 \times 256$6 using observed $256 \times 256$7 such that $256 \times 256$8 for $256 \times 256$9, and then recovers $H_D$0 if feasible (Zhong et al., 2 Sep 2025).

This suggests that QRMark’s robustness does not derive solely from neural extractor tolerance. Rather, the method explicitly separates localized extraction noise from symbol-level message recovery, using training to keep the extractor’s error pattern within the correctable region of the Reed–Solomon code.

4. Adaptive pipeline design and scheduling

QRMark’s system-level optimizations are central to its stated efficiency gains. Detection is organized as a multistage pipeline with GPU stages for preprocessing, tiling, and decoding, while Reed–Solomon correction is CPU-bound (Zhong et al., 2 Sep 2025).

The first optimization is adaptive CUDA stream allocation. The number of CUDA streams per stage, denoted $H_D$1, is adaptively determined based on measured per-stage runtime $H_D$2, per-sample memory $H_D$3, total batch size $H_D$4, available device memory $H_D$5, and stream budget $H_D$6 (Zhong et al., 2 Sep 2025). The stated goal is to equalize the slowest bottleneck stage so as to maximize overlap and throughput; stages with higher workload receive more streams. Unused memory is then allocated by increasing mini-batch sizes for faster stages to balance the pipeline (Zhong et al., 2 Sep 2025). The constraint excerpt provided is

$H_D$7

with the additional stream-budget condition

$H_D$8

The second optimization is tile-based workload interleaving. QRMark overlaps CPU data loading for the next batch, denoted $H_D$9, with current-batch GPU processing, denoted $m$0, in order to minimize pipeline stall time and hide CPU-side image preparation beneath GPU compute (Zhong et al., 2 Sep 2025). It further uses resource-aware mini-batch scheduling, described as assigning image batches to streams according to load predictions using a longest-processing-time first rule while ensuring memory-capacity and load-balancing constraints (Zhong et al., 2 Sep 2025).

The third optimization is co-optimized Reed–Solomon correction. Reed–Solomon decoding is offloaded to a CPU thread pool, with the example of 32 threads given, and runs asynchronously from GPU work via an input queue so that CPU bottlenecks do not stall the GPU (Zhong et al., 2 Sep 2025). A codebook caches mappings from already-seen uncorrected $m$1 outputs to Reed–Solomon–corrected outputs $m$2, thereby avoiding redundant correction for repeated tile outputs (Zhong et al., 2 Sep 2025).

The fourth optimization is preprocessing kernel fusion. Multiple image transformation steps, specifically resize, crop, and normalize, are fused into a single GPU kernel, with Triton given as an example implementation route, thereby reducing unnecessary kernel launches and memory copies (Zhong et al., 2 Sep 2025).

5. Empirical performance and robustness characteristics

The principal end-to-end efficiency result reported for QRMark is an average $m$3 throughput speedup over the baseline Stable Signature detection pipeline (Zhong et al., 2 Sep 2025). The latency scaling behavior is also described: as batch size increases, QRMark’s latency grows much more slowly than the baseline, and at batch size 512, Stable Signature is almost $m$4 slower (Zhong et al., 2 Sep 2025). For AquaLoRA, QRMark offers up to a $m$5 speedup and avoids out-of-memory issues at large batch sizes (Zhong et al., 2 Sep 2025).

GPU utilization figures are reported explicitly. Stable Signature is listed at 32.8%, a naive tiling baseline at 15.6%, and QRMark at 66.7% (Zhong et al., 2 Sep 2025). The optimization breakdown is likewise quantified: naive large batch gives $m$6 speedup, naive tiling gives up to $m$7, and full QRMark with adaptive streams, interleaving, and Reed–Solomon optimization gives $m$8 (Zhong et al., 2 Sep 2025).

Accuracy and robustness are summarized separately. With tile size $m$9, bit-level detection accuracy is stated to be virtually unchanged from the baseline; the example given is 0.999 at $m_s$0 tiles versus 0.999 for full-image detection (Zhong et al., 2 Sep 2025). Under adversarial distortions, QRMark retains high accuracy, with the example of 0.949 bit accuracy at $m_s$1 tiles, whereas accuracy drops more obviously with very small tiles such as $m_s$2 (Zhong et al., 2 Sep 2025). The method is also described as having comparable robustness to attacks, including crop, resize, and jpeg, versus full-image baselines (Zhong et al., 2 Sep 2025).

Payload adaptivity is explicitly limited: QRMark is robust for watermark lengths up to 64 bits per $m_s$3 tile, and beyond this point error correction becomes insufficient (Zhong et al., 2 Sep 2025). For tiling strategy, “random grid” tile selection is reported to offer the best robustness to adversarial attacks among the evaluated tiling strategies (Zhong et al., 2 Sep 2025).

Aspect	Reported result
Average speedup	$m_s$4 over the sequential baseline
GPU utilization	Stable Signature 32.8%, naive tiling 15.6%, QRMark 66.7%
Full-image parity example	0.999 at $m_s$5 tiles vs 0.999 full-image
Adversarial example	0.949 bit accuracy at $m_s$6 tiles
Payload regime	Robust up to 64 bits per $m_s$7 tile

Taken together, these results frame QRMark as a system in which efficiency gains are not presented as a trade against robustness. The paper’s stated contribution is that throughput and scalability improve substantially with minimal accuracy loss (Zhong et al., 2 Sep 2025).

6. Position within watermark detection research

QRMark is situated within image watermark detection for generated images, especially in settings involving diffusion-model outputs and large-scale upload processing (Zhong et al., 2 Sep 2025). The baselines named in the evaluation include Stable Signature and AquaLoRA (Zhong et al., 2 Sep 2025). Within that landscape, QRMark’s stated novelty is the combination of tile-based detection for parallelization and efficiency with QR Code–inspired Reed–Solomon coding to correct the increased tile-level errors produced by tiling, together with loss-function modifications that encourage extractor resilience to within-allowance errors (Zhong et al., 2 Sep 2025).

The paper also emphasizes that prior work in this area has mainly centered on detection accuracy and robustness under transformations and adversarial manipulation, whereas QRMark foregrounds efficiency across large-scale image collections (Zhong et al., 2 Sep 2025). This distinction is central to how the method is positioned: it is both an image-watermark decoder and a deployment-oriented inference pipeline.

A potential misconception is to treat tiling as merely a throughput heuristic. In QRMark, tiling is explicitly coupled to code design, extractor training, and error correction. Another possible misconception is that the Reed–Solomon stage is a minor post-processing step; in the presentation of QRMark, Reed–Solomon coding is the mechanism that makes tile-wise decoding viable without unacceptable degradation (Zhong et al., 2 Sep 2025).

A broader comparison with watermarking in other modalities is possible, but should be made cautiously. For example, watermarking for LLMs has separately emphasized unbiased statistical detection, vocabulary partitioning, and hypothesis testing rather than tile-level decoding or image-space corruption recovery (Chen et al., 16 Feb 2025). This suggests that QRMark belongs to a distinct branch of watermarking research in which geometric decomposition, localized corruption, and heterogeneous CPU/GPU scheduling are first-order concerns.

7. Significance, constraints, and plausible future directions

QRMark’s significance lies in the co-optimization of watermark robustness and systems efficiency. The method is presented as an efficient and adaptive watermark detection system for images, with comprehensive evaluation showing that it significantly outpaces established baselines in throughput and scalability with minimal accuracy loss (Zhong et al., 2 Sep 2025). Its reported scalability to large batches and its avoidance of out-of-memory issues for AquaLoRA at large batch sizes further indicate a deployment orientation rather than a purely algorithmic benchmark focus (Zhong et al., 2 Sep 2025).

At the same time, the published description identifies concrete constraints. Very small tiles, such as $m_s$8, produce more obvious accuracy degradation; payload lengths beyond 64 bits per $m_s$9 tile exceed the regime in which the available error correction remains sufficient; and the effectiveness of the approach depends on coordinated choices of tiling strategy, stream allocation, and Reed–Solomon correction (Zhong et al., 2 Sep 2025). These are not incidental caveats but part of the method’s operating envelope.

A plausible implication is that subsequent work could extend QRMark along several axes already visible in the design: stronger tile-level extractors trained under the same RS-aware objective, broader hardware-aware scheduling policies, or alternative code configurations beyond the appendix example of $\mathcal{D}_m$0 for $\mathcal{D}_m$1 codes (Zhong et al., 2 Sep 2025). Such possibilities, however, remain extrapolations. The published contribution itself is more specific: QRMark combines Reed–Solomon error correction, tile-based detection, adaptive multi-stream allocation, inter-batch interleaving, fused kernels, and asynchronous Reed–Solomon correction into a single end-to-end image watermark detection pipeline (Zhong et al., 2 Sep 2025).